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Technological advances in massively parallel sequencing have led to an exponential growth in the number of known protein sequences. Much of this growth originates from metagenomic projects producing new sequences from environmental and clinical samples. The Unified Human Gastrointestinal Proteome (UHGP) catalogue is one of the most relevant metagenomic datasets with applications ranging from medicine to biology. However, the low levels of sequence annotation may impair its usability. This work aims to produce a family classification of UHGP sequences to facilitate downstream structural and functional annotation. This is achieved through the release of the DPCfam-UHGP50 dataset containing 10,778 putative protein families generated using DPCfam clustering, an unsupervised pipeline grouping sequences into single or multi-domain architectures. DPCfam-UHGP50 considerably improves family coverage at protein and residue levels compared to the manually curated repository Pfam. In the hope that DPCfam-UHGP50 will foster future discoveries in the field of metagenomics of the human gut, we release a FAIR-compliant database of our results that is easily accessible via a searchable web server and Zenodo repository.

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The class I HLA genotype has been widely recognized as a factor influencing HIV disease progression in treatment-naïve subjects. However, little is known regarding its role in HIV disease course and how it influences the size of the viral reservoir once anti-retroviral therapy (ART) is started. Here, leveraging on cutting-edge bioinformatic tools, we explored the relationship between HLA class I and the HIV reservoir in a cohort of 90 people living with HIV (PLWH) undergoing ART and who achieved viral suppression. Analysis of HLA allele distribution among patients with high and low HIV reservoir allowed us to document a predominant role of HLA-B and -C genes in regulating the size of HIV reservoir. We then focused on the analysis of HIV antigen (Ag) repertoire, by investigating immunogenetic parameters such as the degree of homozygosity, HLA evolutionary distance and Ag load. In particular, we used two different bioinformatic algorithms, NetMHCpan and MixMHCpred, to predict HLA presentation of immunogenic HIV-derived peptides and identified HLA-B*57:01 and HLA-B*58:01 among the highest ranking HLAs in terms of total load, suggesting that their previously reported protective role against HIV disease progression might be linked to a more effective viral recognition and presentation to Cytotoxic T lymphocytes (CTLs). Further, we speculated that some peptide-HLA complexes, including those produced by the interaction between HLA-B*27 and the HIV Gag protein, might be particularly relevant for the efficient regulation of HIV replication and containment of the HIV reservoir. Last, we provide evidence of a possible synergistic effect between the CCR5 ∆32 mutation and Ag load in controlling HIV reservoir.

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To study and then harness the tumor-specific T cell dynamics after allogeneic hematopoietic stem cell transplant, we typed the frequency, phenotype, and function of lymphocytes directed against tumor-associated antigens (TAAs) in 39 consecutive transplanted patients, for 1 year after transplant. We showed that TAA-specific T cells circulated in 90% of patients but display a limited effector function associated to an exhaustion phenotype, particularly in the subgroup of patients deemed to relapse, where exhausted stem cell memory T cells accumulated. Accordingly, cancer-specific cytolytic functions were relevant only when the TAA-specific T cell receptors (TCRs) were transferred into healthy, genome-edited T cells. We then exploited trogocytosis and ligandome-on-chip technology to unveil the specificities of tumor-specific TCRs retrieved from the exhausted T cell pool. Overall, we showed that harnessing circulating TAA-specific and exhausted T cells allow to isolate TCRs against TAAs and previously not described acute myeloid leukemia antigens, potentially relevant for T cell-based cancer immunotherapy.

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Uveal melanoma (UM) is the most common ocular malignancy in adults. Nearly 95% of UM patients carry the mutually exclusive mutations in the homologous genes GNAQ (amino acid change Q209L/Q209P) and GNA11 (aminoacid change Q209L). UM is located in an immunosuppressed organ and does not suffer immunoediting. Therefore, we hypothesize that driver mutations in GNAQ/11 genes could be recognized by the immune system. Genomic and transcriptomic data from primary uveal tumors were collected from the TCGA-UM dataset (n = 80) and used to assess the immunogenic potential for GNAQ/GNA11 Q209L/Q209P mutations using a variety of tools and HLA type information. All prediction tools showed stronger GNAQ/11 Q209L binding to HLA than GNAQ/11 Q209P. The immunogenicity analysis revealed that Q209L is likely to be presented by more than 73% of individuals in 1000 G databases whereas Q209P is only predicted to be presented in 24% of individuals. GNAQ/11 Q209L showed a higher likelihood to be presented by HLA-I molecules than almost all driver mutations analyzed. Finally, samples carrying Q209L had a higher immune-reactive phenotype. Regarding cancer risk, seven HLA genotypes with low Q209L affinity show higher frequency in uveal melanoma patients than in the general population. However, no clear association was found between any HLA genotype and survival. Results suggest a high potential immunogenicity of the GNAQ/11 Q209L variant that could allow the generation of novel therapeutic tools to treat UM like neoantigen vaccinations.

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Acute myeloid leukaemia (AML) relapse after allogeneic haematopoietic cell transplantation (allo-HCT) is often driven by immune-related mechanisms and associated with poor prognosis. Immune checkpoint inhibitors combined with hypomethylating agents (HMA) may restore or enhance the graft-versus-leukaemia effect. Still, data about using this combination regimen after allo-HCT are limited. We conducted a prospective, phase II, open-label, single-arm study in which we treated patients with haematological AML relapse after allo-HCT with HMA plus the anti-PD-1 antibody nivolumab. The response was correlated with DNA-, RNA- and protein-based single-cell technology assessments to identify biomarkers associated with therapeutic efficacy. Sixteen patients received a median number of 2 (range 1-7) nivolumab applications. The overall response rate (CR/PR) at day 42 was 25%, and another 25% of the patients achieved stable disease. The median overall survival was 15.6 months. High-parametric cytometry documented a higher frequency of activated (ICOS+ , HLA-DR+ ), low senescence (KLRG1- , CD57- ) CD8+ effector T cells in responders. We confirmed these findings in a preclinical model. Single-cell transcriptomics revealed a pro-inflammatory rewiring of the expression profile of T and myeloid cells in responders. In summary, the study indicates that the post-allo-HCT HMA/nivolumab combination induces anti-AML immune responses in selected patients and could be considered as a bridging approach to a second allo-HCT. Trial-registration: EudraCT-No. 2017-002194-18.

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Current treatment for patients with locally advanced esophageal adenocarcinoma (EAC) is neoadjuvant chemotherapy (nCT), alone or combined with radiotherapy, before surgery. However, fewer than 30% of treated patients show a pathologic complete response to nCT, which correlates with increased 5-year survival compared with nonresponders. Understanding the mechanisms of response to nCT is pivotal to better stratify patients and inform more efficacious therapies. Here, we investigated the immune mechanisms involved in nCT response by multidimensional profiling of pretreatment tumor biopsies and blood from 68 patients with EAC (34 prospectively and 34 retrospectively collected), comparing complete responders versus nonresponders to nCT. At the tumor level, complete response to nCT was associated with molecular signatures of immune response and proliferation, increased putative antitumor tissue-resident memory CD39+ CD103+ CD8+ T cells, and reduced immunosuppressive T regulatory cells (Treg) and M2-like macrophages. Systemically, complete responders showed higher frequencies of immunostimulatory CD14+ CD11c+ HLA-DRhigh cells, and reduced programmed cell death ligand 1-positive (PD-L1+) monocytic myeloid-derived suppressor cells, along with high plasma GM-CSF (proinflammatory) and low IL4, CXCL10, C3a, and C5a (suppressive). Plasma proinflammatory and suppressive cytokines correlated directly and inversely, respectively, with the frequency of tumor-infiltrating CD39+ CD103+ CD8+ T cells. These results suggest that preexisting immunity in baseline tumor drives the clinical activity of nCT in locally advanced EAC. Furthermore, it may be possible to stratify patients based on predictive immune signatures, enabling tailored neoadjuvant and/or adjuvant regimens. SIGNIFICANCE: Multidimensional profiling of pretreatment esophageal adenocarcinoma shows patient response to nCT is correlated with active preexisting immunity and indicates molecular pathways of resistance that may be targeted to improve clinical outcomes.

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Background: The effect of chemoradiation on the anti-cancer immune response is being increasingly acknowledged; however, its clinical implications in treatment responses are yet to be fully understood. Human papillomavirus (HPV)-driven malignancies express viral oncogenic proteins which may serve as tumor-specific antigens and represent ideal candidates for monitoring the peripheral T-cell receptor (TCR) changes secondary to chemoradiotherapy (CRT). Methods: We performed intra-tumoral and pre- and post-treatment peripheral TCR sequencing in a cohort of patients with locally-advanced HPV16-positive cancers treated with CRT. An in silico computational pipeline was used to cluster TCR repertoire based on epitope-specificity and to predict affinity between these clusters and HPV16-derived epitopes. Results: Intra-tumoral repertoire diversity, intra-tumoral and post-treatment peripheral CDR3ß similarity clustering were predictive of response. In responders, CRT triggered an increase peripheral TCR clonality and clonal relatedness. Post-treatment expansion of baseline peripheral dominant TCRs was associated with response. Responders showed more baseline clustered structures of TCRs maintained post-treatment and displayed significantly more maintained clustered structures. When applying clustering by TCR-specificity methods, responders displayed a higher proportion of intra-tumoral TCRs predicted to recognise HPV16 peptides. Conclusions: Baseline TCR characteristics and changes in the peripheral T-cell clones triggered by CRT are associated with treatment outcome. Maintenance and boosting of pre-existing clonotypes are key elements of an effective anti-cancer immune response driven by CRT, supporting a paradigm in which the immune system plays a central role in the success of CRT in current standard-of-care protocols.

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Proteins that are known only at a sequence level outnumber those with an experimental characterization by orders of magnitude. Classifying protein regions (domains) into homologous families can generate testable functional hypotheses for yet unannotated sequences. Existing domain family resources typically use at least some degree of manual curation: they grow slowly over time and leave a large fraction of the protein sequence space unclassified. We here describe automatic clustering by Density Peak Clustering of UniRef50 v. 2017_07, a protein sequence database including approximately 23M sequences. We performed a radical re-implementation of a pipeline we previously developed in order to allow handling millions of sequences and data volumes of the order of 3 TeraBytes. The modified pipeline, which we call DPCfam, finds ∼ 45,000 protein clusters in UniRef50. Our automatic classification is in close correspondence to the ones of the Pfam and ECOD resources: in particular, about 81% of medium-large Pfam families and 72% of ECOD families can be mapped to clusters generated by DPCfam. In addition, our protocol finds more than 14,000 clusters constituted of protein regions with no Pfam annotation, which are therefore candidates for representing novel protein families. These results are made available to the scientific community through a dedicated repository.

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Patients with acute myeloid leukemia (AML) often achieve remission after allogeneic hematopoietic cell transplantation (allo-HCT) but subsequently die of relapse driven by leukemia cells resistant to elimination by allogeneic T cells based on decreased major histocompatibility complex II (MHC-II) expression and apoptosis resistance. Here we demonstrate that mouse-double-minute-2 (MDM2) inhibition can counteract immune evasion of AML. MDM2 inhibition induced MHC class I and II expression in murine and human AML cells. Using xenografts of human AML and syngeneic mouse models of leukemia, we show that MDM2 inhibition enhanced cytotoxicity against leukemia cells and improved survival. MDM2 inhibition also led to increases in tumor necrosis factor-related apoptosis-inducing ligand receptor-1 and -2 (TRAIL-R1/2) on leukemia cells and higher frequencies of CD8+CD27lowPD-1lowTIM-3low T cells, with features of cytotoxicity (perforin+CD107a+TRAIL+) and longevity (bcl-2+IL-7R+). CD8+ T cells isolated from leukemia-bearing MDM2 inhibitor-treated allo-HCT recipients exhibited higher glycolytic activity and enrichment for nucleotides and their precursors compared with vehicle control subjects. T cells isolated from MDM2 inhibitor-treated AML-bearing mice eradicated leukemia in secondary AML-bearing recipients. Mechanistically, the MDM2 inhibitor-mediated effects were p53-dependent because p53 knockdown abolished TRAIL-R1/2 and MHC-II upregulation, whereas p53 binding to TRAILR1/2 promotors increased upon MDM2 inhibition. The observations in the mouse models were complemented by data from human individuals. Patient-derived AML cells exhibited increased TRAIL-R1/2 and MHC-II expression on MDM2 inhibition. In summary, we identified a targetable vulnerability of AML cells to allogeneic T-cell-mediated cytotoxicity through the restoration of p53-dependent TRAIL-R1/2 and MHC-II production via MDM2 inhibition.

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The SYLF domain is an evolutionary conserved protein domain with phosphatidylinositol binding ability, whose three-dimensional structure is unknown. Here, we present the solution structure and the dynamics characterization of the SYLF domain of the bacterial BPSL1445 protein. BPSL1445 is a seroreactive antigen and a diagnostic marker of Burkholderia pseudomallei, the etiological agent of melioidosis, a severe infectious disease in the tropics. The BPSL1445 SYLF domain (BPSL1445-SYLF) consists of a ß-barrel core, with two flexible loops protruding out of the barrel and three helices packing on its surface. Our structure allows for a more precise definition of the boundaries of the SYLF domain compared to the previously reported one and suggests common ancestry with bacterial EipA domains. We also demonstrate by phosphatidyl-inositol phosphate arrays and nuclear magnetic resonance titrations that BPSL1445-SYLF weakly interacts with phosphoinositides, thus supporting lipid binding abilities of this domain also in prokaryotes.

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BACKGROUND: The identification of protein families is of outstanding practical importance for in silico protein annotation and is at the basis of several bioinformatic resources. Pfam is possibly the most well known protein family database, built in many years of work by domain experts with extensive use of manual curation. This approach is generally very accurate, but it is quite time consuming and it may suffer from a bias generated from the hand-curation itself, which is often guided by the available experimental evidence. RESULTS: We introduce a procedure that aims to identify automatically putative protein families. The procedure is based on Density Peak Clustering and uses as input only local pairwise alignments between protein sequences. In the experiment we present here, we ran the algorithm on about 4000 full-length proteins with at least one domain classified by Pfam as belonging to the Pseudouridine synthase and Archaeosine transglycosylase (PUA) clan. We obtained 71 automatically-generated sequence clusters with at least 100 members. While our clusters were largely consistent with the Pfam classification, showing good overlap with either single or multi-domain Pfam family architectures, we also observed some inconsistencies. The latter were inspected using structural and sequence based evidence, which suggested that the automatic classification captured evolutionary signals reflecting non-trivial features of protein family architectures. Based on this analysis we identified a putative novel pre-PUA domain as well as alternative boundaries for a few PUA or PUA-associated families. As a first indication that our approach was unlikely to be clan-specific, we performed the same analysis on the P53 clan, obtaining comparable results. CONCLUSIONS: The clustering procedure described in this work takes advantage of the information contained in a large set of pairwise alignments and successfully identifies a set of putative families and family architectures in an unsupervised manner. Comparison with the Pfam classification highlights significant overlap and points to interesting differences, suggesting that our new algorithm could have potential in applications related to automatic protein classification. Testing this hypothesis, however, will require further experiments on large and diverse sequence datasets.

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Cancer somatic mutations have been identified as a source of antigens that can be targeted by cancer immunotherapy. In this work, expanding on previous studies, we analyze the HLA-presentation properties of mutations that are known to drive resistance to cancer targeted-therapies. We survey a large dataset of mutations that confer resistance to different drugs and occur in numerous genes and tumor types. We show that a significant number of them are predicted in silico to be potentially immunogenic across a large proportion of the human population. Further, by analyzing a cohort of patients carrying a small subset of these resistance mutations, we provide evidence that what is observed in the general population may be indicative of the mutations' immunogenic potential in resistant patients. Two of the mutations in our dataset had previously been experimentally validated by others and it was confirmed that some of their associated neopeptides elicit T-cell responses in vitro. The identification of potent cancer-specific antigens can be instrumental for developing more effective immunotherapies. In this work, we propose a novel list of drug-resistance mutations, several of which are recurrent, that could be of particular interest in the context of off-the-shelf precision immunotherapies such as therapeutic cancer vaccines.

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Reversion mutations in BRCA1 or BRCA2 are associated with resistance to PARP inhibitors and platinum. To better understand the nature of these mutations, we collated, codified, and analyzed more than 300 reversions. This identified reversion "hotspots" and "deserts" in regions encoding the N and C terminus, respectively, of BRCA2, suggesting that pathogenic mutations in these regions may be at higher or lower risk of reversion. Missense and splice-site pathogenic mutations in BRCA1/2 also appeared less likely to revert than truncating mutations. Most reversions were <100 bp deletions. Although many deletions exhibited microhomology, this was not universal, suggesting that multiple DNA-repair processes cause reversion. Finally, we found that many reversions were predicted to encode immunogenic neopeptides, suggesting a route to the treatment of reverted disease. As well as providing a freely available database for the collation of future reversion cases, these observations have implications for how drug resistance might be managed in BRCA-mutant cancers. SIGNIFICANCE: Reversion mutations in BRCA genes are a major cause of clinical platinum and PARP inhibitor resistance. This analysis of all reported clinical reversions suggests that the position of BRCA2 mutations affects the risk of reversion. Many reversions are also predicted to encode tumor neoantigens, providing a potential route to targeting resistance.This article is highlighted in the In This Issue feature, p. 1426.

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An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Mismatch repair deficient (dMMR) gastro-oesophageal adenocarcinomas (GOAs) show better outcomes than their MMR-proficient counterparts and high immunotherapy sensitivity. The hypermutator-phenotype of dMMR tumours theoretically enables high evolvability but their evolution has not been investigated. Here we apply multi-region exome sequencing (MSeq) to four treatment-naive dMMR GOAs. This reveals extreme intratumour heterogeneity (ITH), exceeding ITH in other cancer types >20-fold, but also long phylogenetic trunks which may explain the exquisite immunotherapy sensitivity of dMMR tumours. Subclonal driver mutations are common and parallel evolution occurs in RAS, PIK3CA, SWI/SNF-complex genes and in immune evasion regulators. MSeq data and evolution analysis of single region-data from 64 MSI GOAs show that chromosome 8 gains are early genetic events and that the hypermutator-phenotype remains active during progression. MSeq may be necessary for biomarker development in these heterogeneous cancers. Comparison with other MSeq-analysed tumour types reveals mutation rates and their timing to determine phylogenetic tree morphologies.

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BACKGROUND: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). METHODS: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to model position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson model-based statistical framework for SNV detection. RESULTS: Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic positions and compare the results to digital droplet PCR experiments. CONCLUSIONS: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .

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Despite biomarker stratification, the anti-EGFR antibody cetuximab is only effective against a subgroup of colorectal cancers (CRCs). This genomic and transcriptomic analysis of the cetuximab resistance landscape in 35 RAS wild-type CRCs identified associations of NF1 and non-canonical RAS/RAF aberrations with primary resistance and validated transcriptomic CRC subtypes as non-genetic predictors of benefit. Sixty-four percent of biopsies with acquired resistance harbored no genetic resistance drivers. Most of these had switched from a cetuximab-sensitive transcriptomic subtype at baseline to a fibroblast- and growth factor-rich subtype at progression. Fibroblast-supernatant conferred cetuximab resistance in vitro, confirming a major role for non-genetic resistance through stromal remodeling. Cetuximab treatment increased cytotoxic immune infiltrates and PD-L1 and LAG3 immune checkpoint expression, potentially providing opportunities to treat cetuximab-resistant CRCs with immunotherapy.

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How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.

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Sulfur, most abundantly found in the environment as sulfate (SO42-), is an essential element in metabolites required by all living cells, including amino acids, co-factors and vitamins. However, current understanding of the cellular delivery of SO42- at the molecular level is limited. CysZ has been described as a SO42- permease, but its sequence family is without known structural precedent. Based on crystallographic structure information, SO42- binding and flux experiments, we provide insight into the molecular mechanism of CysZ-mediated translocation of SO42- across membranes. CysZ structures from three different bacterial species display a hitherto unknown fold and have subunits organized with inverted transmembrane topology. CysZ from Pseudomonas denitrificans assembles as a trimer of antiparallel dimers and the CysZ structures from two other species recapitulate dimers from this assembly. Mutational studies highlight the functional relevance of conserved CysZ residues.

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Advances in DNA sequencing technologies have led to an increasing amount of protein sequence data being generated. Only a small fraction of this protein sequence data will have experimental annotation associated with them. Here, we describe a protocol for in silico homology-based annotation of large protein datasets that makes extensive use of manually curated collections of protein families. We focus on annotations provided by the Pfam database and suggest ways to identify family outliers and family variations. This protocol may be useful to people who are new to protein data analysis, or who are unfamiliar with the current computational tools that are available.

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